When a camera captures an image, light from a three-dimensional scene is focused and captured on a two dimensional image plane. Thus, each pixel on the image plane corresponds to a column of light from the original scene. While in an ideal pinhole camera, a simple projection matrix may be sufficient for this calculation, in practice, errors resulting from misaligned lenses and deformations in the structure of the lenses can result in complex distortions in the final image.
Camera calibration includes the process of determining the internal camera geometric and optical characteristics (intrinsic parameters) and/or the three-dimensional position and orientation of the camera frame relative to a certain world coordinate system (extrinsic parameters). In many cases, the overall performance of the camera system depends on the accuracy of the camera calibration.
Several methods for geometric camera calibration are known. One approach minimizes a nonlinear error function. A camera projection matrix is derived from the intrinsic and extrinsic parameters of the camera, and is often represented by a series of transformations; e.g., a matrix of camera intrinsic parameters, a rotation matrix, and a translation vector. The camera projection matrix can be used to associate points in a camera's image space with locations in three-dimensional world space.